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. 2023 Mar 28;10:173. doi: 10.1038/s41597-023-01984-9

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© UT-Battelle, LLC 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 4 — Redocking RMSD distribution against the Mpro Fragalysis dataset (left) and the AutoDock-GPU set of 42 (right), measuring the deviation between a predicted docking pose and an actual crystal structure. The fragalysis dataset (left) docks 426 ligands where bound crystal structures are available from the Diamond dataset, and compared the displacement for only the top-scoring pose from AutoDock Vina (blue)⁵², the current version of AutoDock-GPU (red, 1.5.3)¹⁵ and the version of AutoDock-GPU used to generate the dataset (black, June 2020). Beyond the top poses, the distribution for all 20 predicted poses for both versions of AutoDock-GPU are given as a dashed line. The set of 42 (right) similarly compares the cumulative RMSD distribution generated after redocking.